International trade shapes global mercury–related health impacts

Abstract Mercury (Hg) is a strong neurotoxin with substantial dangers to human health. Hg undergoes active global cycles, and the emission sources there of can also be geographically relocated through economic trade. Through investigation of a longer chain of the global biogeochemical Hg cycle from economic production to human health, international cooperation on Hg control strategies in Minamata Convention can be facilitated. In the present study, four global models are combined to investigate the effect of international trade on the relocation of Hg emissions, pollution, exposure, and related human health impacts across the world. The results show that 47% of global Hg emissions are related to commodities consumed outside of the countries where the emissions are produced, which has largely influenced the environmental Hg levels and human exposure thereto across the world. Consequently, international trade is found to enable the whole world to avoid 5.7 × 105 points for intelligence quotient (IQ) decline and 1,197 deaths from fatal heart attacks, saving a total of $12.5 billion (2020 USD) in economic loss. Regionally, international trade exacerbates Hg challenges in less developed countries, while resulting in an alleviation in developed countries. The change in economic loss therefore varies from the United States (−$4.0 billion) and Japan (−$2.4 billion) to China (+$2.7 billion). The present results reveal that international trade is a critical factor but might be largely overlooked in global Hg pollution mitigation.

(1) The compilation of production-based Hg emission inventory is uncertain due to knowledge gaps regarding emission factor (e.g., Hg concentrations in fuel/raw materials), and activity rates. The uncertainties in both the activity data and emission factors used in the EDGARv4 mercury emission inventory are estimated using the default methodology recommended by IPCC (IPCC, 2006) with the lower and upper bounds of the 95% confidence interval from EMEP/EEA (EMEP/EEA, 2009) expressed as a percent relative to the mean for emission factors. The estimated uncertainty when combining the cement production, metal industry, combustion and waste incineration sectors ranges from −26 to +33% for OECD countries and Economies in Transition (Russia Federation, Ukraine and other eastern European countries) countries and −33 to +42% for Non-Annex I countries. Moreover, the uncertainty in the mercury emission inventory is expected to be much higher when considering the uncertainty in the activity data for artisanal and small scale gold production which is difficult to calculate due to the lack of official statistics in most countries (Muntean et al., 2014). However, updated activity data for sinter production in the iron & steel industry and new activity data for the ASGM sector from the Artisanal Gold Council (AGC, 2010) leads to an improvement in the uncertainty of EDGARv4.tox2 (Muntean et al., 2018). An uncertainty of [−33%, 42%] is used for the production-based emission inventory in this study.
(2) Estimates of emissions embodied in trade share the most uncertainties with production-based Hg emissions and contain an additional uncertainty from the MRIO model associated with inaccuracies in economic statistics, sectoral mapping, and data harmonization (Lenzen et al., 2010). Herwtich and Peters (2009) reported the coefficient of variation (CV) of trade coefficients have 10%-20% for GTAP-MRIO table. Based on the finding, Zhang et al. (2017) estimated uncertainty for consumption-based air pollution emissions for nations by adding a 13% of uncertainty to production-based emissions. A recent study reported uncertainty of Chinese province mercury footprint varying from 8% to 34% by using Monte Carlo simulation method (Zhang et al., 2019a). As the uncertainty for small emitters is larger than that of large emitters (Lenzen et al., 2010), the uncertainty for national mercury footprint in our study should be lower than 34%.
(3) Environmental Hg levels simulated by the comprehensive mercury transport model is affected by errors in emission inputs and the model representation of tropospheric chemical processes, especially Hg chemistry and physical processes such as vertical transport and wet scavenging. The modeled values in terms of surface total gaseous mercury, atmospheric wet deposition and marine surface MeHg concentration are comparable with the corresponding available observations (Figure S11), given a relatively large uncertainty range (Zhang et al., 2019b). Meanwhile, following the spirit of Chen et al. (2019) and Zhang et al. (2017), we apply the normalized root-mean-square deviation (NRMSD) between the simulations and observations over measurement sites to represent the uncertainties derived from the mercury transport model. The estimations are 29.1%, 62.4%, and 210.6% for surface total gaseous mercury, atmospheric wet deposition and marine surface MeHg concentration, respectively.
(4) The compilation of intake inventory of MeHg is subject to uncertainties in the MeHg concentrations of food products and intake rate of food products. MeHg concentrations of food products are collected from the literature. We use the variability of the log-transformed concentrations in each food category to represent its uncertainty (Zhang et al., 2021) and the estimation is [−37%, 63%]. We rely on the database of the United Nations' Food and Agriculture Organization (FAO) for food consumption. Compared with national data, the two data sources generally agree within a factor of 2. This reflects both the different survey methods and variability among the population (Cook et al., 2000;Grandjean, 2012). Based on the comparison between the food consumption data from UN FAO and national datasets (Zhang et al., 2021), the difference between them is used to represent the uncertainty range of food consumption and the estimation is [−47%, 42%].
(5) The evaluation of human health impacts due to MeHg intake is subject to uncertainties in parameters used in the evaluation and the total deaths from fatal heart attacks collected from national statistics. We use the ranges (or standard deviations) of the dose-effect relationship between MeHg exposure and its health effect summarized by Chen et al. (2019) and Giang & Selin (2016), and the uncertainty estimation is [−59%, 147%]. For per-IQ earn loss, we use a high-and low-end value of $18,832 and $8013, respectively. The VSL per death ranges from $1 to $10 million following Giang & Selin (Chen et al., 2019). These economic valuation parameters lead to an uncertainty of [−70%, 26%] for economic loss.
Taken together these suggest greater uncertainty in estimates of the health impacts of specific scenarios. Ideally, we would have conducted a formal analysis of the propagation of uncertainty through our complete set of models (activity => emissions => concentrations => exposure => health impacts). Unfortunately, the computing time necessary to conduct such an analysis is prohibitive. Therefore, we consider the contribution of the data and parameters for food consumption, food MeHg concentrations, dose-effect relationship, and economic valuation to the total uncertainty. The overall uncertainty is estimated by a Monte Carlo approach. The health risk calculation is repeated for 1000 times with randomly sampled parameters for these four factors. The 2.5% and 97.5% percentiles of the calculated risk are taken as the overall uncertainty range (i.e., 95% confidence interval). The overall uncertainty is estimated at [−75.3%, 131.6%].

Limitations and prospects
In addition to the uncertainties discussed above, there are additional limitations in this study. Previous studies have shown that ecosystems can respond to changes in Hg inputs on timescales of years to decades (Vijayaraghavan et al., 2014;Selin et al., 2010). Hg concentrations in food products collected from the literature in this study were measured during recent decades and are used to represent the average condition of Hg risks in these decades. Hg emissions and environmental Hg pollution in 2011 can represent an average condition of Hg pollution during the last few decades. We establish relationships between Hg emissions and risks during the last few decades and do not consider the accurate lag time in the response between Hg emissions and risks for a specific time period. Additional measurements of yearly data on Hg concentrations in food products and accurate lag time in the response between Hg emissions and risks are needed in the future. Additionally, we assume that the food MeHg concentrations respond linearly to the level of Hg added to the ecosystem, which is an upper limit or a conservative estimation of the response relationship. Indeed, an ecosystem-level experimental study reveals a concaved curve in fish MeHg concentrations responding to the added Hg that is linearly increasing in a 15-year course (Blanchfield et al., 2022). The assumption can be improved when more data are available in the future. We consider no time lags among environmental levels, food concentrations, and human exposure, which might be acceptable as most of the food items (e.g., rice, aqua-and mariculture) are harvested and consumed within a few years. Moreover, the ecosystem-level study also shows a timely response of fish MeHg to the addition of Hg to the environment (Giang & Selin, 2016).
This study develops a more comprehensive assessment method to investigate the chain of the biogeochemical Hg cycle from economic activities to human health at the global level. In addition to economic supply chains and Hg emission sources, the biogeochemical Hg cycle and related adverse health impacts (especially the human exposures) are also influenced by multiple extrinsic and intrinsic factors. The extrinsic factors include climate change, land use change, hydrologic management, invasive species, and food consumption & dietary habits. The intrinsic factors include genetics, gastrointestinal assimilation, microbiome, nutrients & co-exposures to other contaminants, and co-exposures to other diseases (Grandjean, 2012). However, this study aims to identify the role of international trade in relocation of global Hg emissions, pollution, exposure, and related health burden. Therefore, these extrinsic and intrinsic factors are assumed to be consistent under the "with trade" and "no trade" scenarios, and have not been taken into consideration in this study. Moreover, the mechanisms of some intrinsic factors (e.g., microbiome) still remain unknown (Eagles-Smith et al., 2018;Madan et al., 2012), which prevents the adoption of these intrinsic factors in the assessment of Hg-related health risks, especially for studies on large human communities at the macro scale (Grandjean, 2012). Nevertheless, the values of parameters describing certain extrinsic factors can be incorporated into the model to track the changes in Hg cycle and related health risks in the context of rapid global changes. Meanwhile, the intrinsic factors can also be practicably incorporated into the model, when future studies can clearly characterize their dynamics, mechanisms, and modelling methods. Figure S1. Bilateral flows of Hg emissions embodied in the international trade between 13 world regions. The arc length of each region denotes Hg emissions embodied in its exports and imports. The chord linking two regions provides the information for their embodied Hg flows to each other. The width of links denotes the magnitude of Hg transfer flows. In the clockwise direction of the diagram, the links show outflows first, followed by inflows. Hg transfer from a source region is indicated with a link emanating from the arc of the same color. A recipient region is indicated with an arc from the link of a different color.    Figure S5. Spatial distribution of Hg emission inventory under "with trade" and "no trade" scenarios and the differences therebetween. The rows (A-C), (D-F), and (G-I) are for gaseous elemental, gaseous oxidized, and particle-bound Hg, respectively. For each row, the three panels denote the spatial distribution of Hg emissions for "with trade" and "no trade" scenarios and the differences therebetween.      (Zhang & Zhang, 2022;Zhang et al., 2020). Table S1. Description of "with trade" and "no trade" scenarios.

Scenarios Description Emissions
"with trade" The actual scenario with the existence of international trade Regional emissions accounted by production "no trade" The counterfactual scenario with an absence of international trade Regional emissions accounted by consumption Note: The global total emission quantity under "with trade" and "no trade" scenarios are equal.  Non-road transport Non-road transport Transformation industry Manufacture of coke and refined petroleum products Road transport Land transport and transport via pipelines Note: The emissions from "Small combustion: buildings" and "International shipping (see SEA)" are assumed to remain unchanged under production-and consumption-based accounting, and therefore not incorporated into the MRIO model. The emissions from "Combustion in industry" are disaggregated to 19 related industries in each country based on the Hg emission inventory obtained from Eora which provides sector-level Hg emissions from energy combustion for each country.